Overview

Dataset statistics

Number of variables28
Number of observations1200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory262.6 KiB
Average record size in memory224.1 B

Variable types

Categorical15
Numeric11
Boolean2

Alerts

EmpNumber has a high cardinality: 1200 distinct valuesHigh cardinality
Age is highly overall correlated with TotalWorkExperienceInYearsHigh correlation
TotalWorkExperienceInYears is highly overall correlated with Age and 2 other fieldsHigh correlation
ExperienceYearsAtThisCompany is highly overall correlated with TotalWorkExperienceInYears and 3 other fieldsHigh correlation
ExperienceYearsInCurrentRole is highly overall correlated with ExperienceYearsAtThisCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with ExperienceYearsAtThisCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with ExperienceYearsAtThisCompany and 1 other fieldsHigh correlation
EmpDepartment is highly overall correlated with EmpJobRoleHigh correlation
EmpJobRole is highly overall correlated with EmpDepartmentHigh correlation
EmpJobLevel is highly overall correlated with TotalWorkExperienceInYearsHigh correlation
EmpNumber is uniformly distributedUniform
EmpNumber has unique valuesUnique
NumCompaniesWorked has 156 (13.0%) zerosZeros
TrainingTimesLastYear has 44 (3.7%) zerosZeros
ExperienceYearsAtThisCompany has 36 (3.0%) zerosZeros
ExperienceYearsInCurrentRole has 190 (15.8%) zerosZeros
YearsSinceLastPromotion has 469 (39.1%) zerosZeros
YearsWithCurrManager has 215 (17.9%) zerosZeros

Reproduction

Analysis started2023-07-30 02:57:49.230049
Analysis finished2023-07-30 02:58:04.907530
Duration15.68 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

EmpNumber
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct1200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
E1001000
 
1
E100346
 
1
E100342
 
1
E100341
 
1
E100340
 
1
Other values (1195)
1195 

Length

Max length8
Median length8
Mean length7.6375
Min length7

Characters and Unicode

Total characters9165
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1200 ?
Unique (%)100.0%

Sample

1st rowE1001000
2nd rowE1001006
3rd rowE1001007
4th rowE1001009
5th rowE1001010

Common Values

ValueCountFrequency (%)
E1001000 1
 
0.1%
E100346 1
 
0.1%
E100342 1
 
0.1%
E100341 1
 
0.1%
E100340 1
 
0.1%
E100339 1
 
0.1%
E100338 1
 
0.1%
E100335 1
 
0.1%
E100334 1
 
0.1%
E100333 1
 
0.1%
Other values (1190) 1190
99.2%

Length

2023-07-30T08:28:04.990298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e1001000 1
 
0.1%
e1001040 1
 
0.1%
e1001009 1
 
0.1%
e1001010 1
 
0.1%
e1001011 1
 
0.1%
e1001016 1
 
0.1%
e1001019 1
 
0.1%
e1001020 1
 
0.1%
e1001021 1
 
0.1%
e1001022 1
 
0.1%
Other values (1190) 1190
99.2%

Most occurring characters

ValueCountFrequency (%)
0 2752
30.0%
1 2113
23.1%
E 1200
13.1%
2 602
 
6.6%
3 403
 
4.4%
5 358
 
3.9%
7 356
 
3.9%
8 355
 
3.9%
4 346
 
3.8%
9 345
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7965
86.9%
Uppercase Letter 1200
 
13.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2752
34.6%
1 2113
26.5%
2 602
 
7.6%
3 403
 
5.1%
5 358
 
4.5%
7 356
 
4.5%
8 355
 
4.5%
4 346
 
4.3%
9 345
 
4.3%
6 335
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
E 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7965
86.9%
Latin 1200
 
13.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2752
34.6%
1 2113
26.5%
2 602
 
7.6%
3 403
 
5.1%
5 358
 
4.5%
7 356
 
4.5%
8 355
 
4.5%
4 346
 
4.3%
9 345
 
4.3%
6 335
 
4.2%
Latin
ValueCountFrequency (%)
E 1200
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2752
30.0%
1 2113
23.1%
E 1200
13.1%
2 602
 
6.6%
3 403
 
4.4%
5 358
 
3.9%
7 356
 
3.9%
8 355
 
3.9%
4 346
 
3.8%
9 345
 
3.8%

Age
Real number (ℝ)

Distinct43
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.918333
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-30T08:28:05.079122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.0872885
Coefficient of variation (CV)0.24614569
Kurtosis-0.43099958
Mean36.918333
Median Absolute Deviation (MAD)6
Skewness0.38414496
Sum44302
Variance82.578813
MonotonicityNot monotonic
2023-07-30T08:28:05.173398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
34 71
 
5.9%
35 64
 
5.3%
36 60
 
5.0%
31 57
 
4.8%
29 51
 
4.2%
38 48
 
4.0%
32 46
 
3.8%
40 46
 
3.8%
33 46
 
3.8%
27 43
 
3.6%
Other values (33) 668
55.7%
ValueCountFrequency (%)
18 8
 
0.7%
19 8
 
0.7%
20 6
 
0.5%
21 11
 
0.9%
22 15
 
1.2%
23 9
 
0.8%
24 20
1.7%
25 24
2.0%
26 33
2.8%
27 43
3.6%
ValueCountFrequency (%)
60 3
 
0.2%
59 6
 
0.5%
58 11
0.9%
57 4
 
0.3%
56 11
0.9%
55 17
1.4%
54 16
1.3%
53 15
1.2%
52 15
1.2%
51 14
1.2%

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Male
725 
Female
475 

Length

Max length6
Median length4
Mean length4.7916667
Min length4

Characters and Unicode

Total characters5750
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 725
60.4%
Female 475
39.6%

Length

2023-07-30T08:28:05.270687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:05.378089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
male 725
60.4%
female 475
39.6%

Most occurring characters

ValueCountFrequency (%)
e 1675
29.1%
a 1200
20.9%
l 1200
20.9%
M 725
12.6%
F 475
 
8.3%
m 475
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4550
79.1%
Uppercase Letter 1200
 
20.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1675
36.8%
a 1200
26.4%
l 1200
26.4%
m 475
 
10.4%
Uppercase Letter
ValueCountFrequency (%)
M 725
60.4%
F 475
39.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5750
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1675
29.1%
a 1200
20.9%
l 1200
20.9%
M 725
12.6%
F 475
 
8.3%
m 475
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1675
29.1%
a 1200
20.9%
l 1200
20.9%
M 725
12.6%
F 475
 
8.3%
m 475
 
8.3%
Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Life Sciences
492 
Medical
384 
Marketing
137 
Technical Degree
100 
Other
66 

Length

Max length16
Median length15
Mean length10.468333
Min length5

Characters and Unicode

Total characters12562
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarketing
2nd rowMarketing
3rd rowLife Sciences
4th rowHuman Resources
5th rowMarketing

Common Values

ValueCountFrequency (%)
Life Sciences 492
41.0%
Medical 384
32.0%
Marketing 137
 
11.4%
Technical Degree 100
 
8.3%
Other 66
 
5.5%
Human Resources 21
 
1.8%

Length

2023-07-30T08:28:05.441147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:05.535475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
life 492
27.1%
sciences 492
27.1%
medical 384
21.2%
marketing 137
 
7.6%
technical 100
 
5.5%
degree 100
 
5.5%
other 66
 
3.6%
human 21
 
1.2%
resources 21
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 2505
19.9%
i 1605
12.8%
c 1589
12.6%
n 750
 
6.0%
a 642
 
5.1%
613
 
4.9%
s 534
 
4.3%
M 521
 
4.1%
L 492
 
3.9%
f 492
 
3.9%
Other values (16) 2819
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10136
80.7%
Uppercase Letter 1813
 
14.4%
Space Separator 613
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2505
24.7%
i 1605
15.8%
c 1589
15.7%
n 750
 
7.4%
a 642
 
6.3%
s 534
 
5.3%
f 492
 
4.9%
l 484
 
4.8%
d 384
 
3.8%
r 324
 
3.2%
Other values (7) 827
 
8.2%
Uppercase Letter
ValueCountFrequency (%)
M 521
28.7%
L 492
27.1%
S 492
27.1%
T 100
 
5.5%
D 100
 
5.5%
O 66
 
3.6%
H 21
 
1.2%
R 21
 
1.2%
Space Separator
ValueCountFrequency (%)
613
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11949
95.1%
Common 613
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2505
21.0%
i 1605
13.4%
c 1589
13.3%
n 750
 
6.3%
a 642
 
5.4%
s 534
 
4.5%
M 521
 
4.4%
L 492
 
4.1%
f 492
 
4.1%
S 492
 
4.1%
Other values (15) 2327
19.5%
Common
ValueCountFrequency (%)
613
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2505
19.9%
i 1605
12.8%
c 1589
12.6%
n 750
 
6.0%
a 642
 
5.1%
613
 
4.9%
s 534
 
4.3%
M 521
 
4.1%
L 492
 
3.9%
f 492
 
3.9%
Other values (16) 2819
22.4%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Married
548 
Single
384 
Divorced
268 

Length

Max length8
Median length7
Mean length6.9033333
Min length6

Characters and Unicode

Total characters8284
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowMarried
4th rowDivorced
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 548
45.7%
Single 384
32.0%
Divorced 268
22.3%

Length

2023-07-30T08:28:05.629728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:05.708394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
married 548
45.7%
single 384
32.0%
divorced 268
22.3%

Most occurring characters

ValueCountFrequency (%)
r 1364
16.5%
i 1200
14.5%
e 1200
14.5%
d 816
9.9%
M 548
6.6%
a 548
6.6%
S 384
 
4.6%
n 384
 
4.6%
g 384
 
4.6%
l 384
 
4.6%
Other values (4) 1072
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7084
85.5%
Uppercase Letter 1200
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1364
19.3%
i 1200
16.9%
e 1200
16.9%
d 816
11.5%
a 548
7.7%
n 384
 
5.4%
g 384
 
5.4%
l 384
 
5.4%
v 268
 
3.8%
o 268
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
M 548
45.7%
S 384
32.0%
D 268
22.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 8284
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1364
16.5%
i 1200
14.5%
e 1200
14.5%
d 816
9.9%
M 548
6.6%
a 548
6.6%
S 384
 
4.6%
n 384
 
4.6%
g 384
 
4.6%
l 384
 
4.6%
Other values (4) 1072
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1364
16.5%
i 1200
14.5%
e 1200
14.5%
d 816
9.9%
M 548
6.6%
a 548
6.6%
S 384
 
4.6%
n 384
 
4.6%
g 384
 
4.6%
l 384
 
4.6%
Other values (4) 1072
12.9%

EmpDepartment
Categorical

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Sales
373 
Development
361 
Research & Development
343 
Human Resources
54 
Finance
49 

Length

Max length22
Median length15
Mean length12.3125
Min length5

Characters and Unicode

Total characters14775
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowSales
3rd rowSales
4th rowHuman Resources
5th rowSales

Common Values

ValueCountFrequency (%)
Sales 373
31.1%
Development 361
30.1%
Research & Development 343
28.6%
Human Resources 54
 
4.5%
Finance 49
 
4.1%
Data Science 20
 
1.7%

Length

2023-07-30T08:28:05.802595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:05.881112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
development 704
35.9%
sales 373
19.0%
research 343
17.5%
343
17.5%
human 54
 
2.8%
resources 54
 
2.8%
finance 49
 
2.5%
data 20
 
1.0%
science 20
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 3368
22.8%
l 1077
 
7.3%
n 876
 
5.9%
a 859
 
5.8%
s 824
 
5.6%
760
 
5.1%
o 758
 
5.1%
m 758
 
5.1%
t 724
 
4.9%
D 724
 
4.9%
Other values (12) 4047
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12055
81.6%
Uppercase Letter 1617
 
10.9%
Space Separator 760
 
5.1%
Other Punctuation 343
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3368
27.9%
l 1077
 
8.9%
n 876
 
7.3%
a 859
 
7.1%
s 824
 
6.8%
o 758
 
6.3%
m 758
 
6.3%
t 724
 
6.0%
v 704
 
5.8%
p 704
 
5.8%
Other values (5) 1403
11.6%
Uppercase Letter
ValueCountFrequency (%)
D 724
44.8%
R 397
24.6%
S 393
24.3%
H 54
 
3.3%
F 49
 
3.0%
Space Separator
ValueCountFrequency (%)
760
100.0%
Other Punctuation
ValueCountFrequency (%)
& 343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13672
92.5%
Common 1103
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3368
24.6%
l 1077
 
7.9%
n 876
 
6.4%
a 859
 
6.3%
s 824
 
6.0%
o 758
 
5.5%
m 758
 
5.5%
t 724
 
5.3%
D 724
 
5.3%
v 704
 
5.1%
Other values (10) 3000
21.9%
Common
ValueCountFrequency (%)
760
68.9%
& 343
31.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3368
22.8%
l 1077
 
7.3%
n 876
 
5.9%
a 859
 
5.8%
s 824
 
5.6%
760
 
5.1%
o 758
 
5.1%
m 758
 
5.1%
t 724
 
4.9%
D 724
 
4.9%
Other values (12) 4047
27.4%

EmpJobRole
Categorical

Distinct19
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Sales Executive
270 
Developer
236 
Manager R&D
94 
Research Scientist
77 
Sales Representative
69 
Other values (14)
454 

Length

Max length25
Median length21.5
Mean length14.545
Min length7

Characters and Unicode

Total characters17454
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Executive
2nd rowSales Executive
3rd rowSales Executive
4th rowManager
5th rowSales Executive

Common Values

ValueCountFrequency (%)
Sales Executive 270
22.5%
Developer 236
19.7%
Manager R&D 94
 
7.8%
Research Scientist 77
 
6.4%
Sales Representative 69
 
5.8%
Laboratory Technician 64
 
5.3%
Senior Developer 52
 
4.3%
Manager 51
 
4.2%
Finance Manager 49
 
4.1%
Human Resources 45
 
3.8%
Other values (9) 193
16.1%

Length

2023-07-30T08:28:05.975396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sales 339
15.9%
developer 288
13.5%
executive 270
12.7%
manager 221
10.4%
r&d 109
 
5.1%
representative 102
 
4.8%
scientist 97
 
4.6%
research 96
 
4.5%
senior 67
 
3.1%
laboratory 64
 
3.0%
Other values (14) 475
22.3%

Most occurring characters

ValueCountFrequency (%)
e 3179
18.2%
a 1536
 
8.8%
r 1136
 
6.5%
i 975
 
5.6%
928
 
5.3%
c 907
 
5.2%
n 901
 
5.2%
t 900
 
5.2%
s 788
 
4.5%
l 733
 
4.2%
Other values (24) 5471
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14180
81.2%
Uppercase Letter 2237
 
12.8%
Space Separator 928
 
5.3%
Other Punctuation 109
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3179
22.4%
a 1536
10.8%
r 1136
 
8.0%
i 975
 
6.9%
c 907
 
6.4%
n 901
 
6.4%
t 900
 
6.3%
s 788
 
5.6%
l 733
 
5.2%
v 672
 
4.7%
Other values (11) 2453
17.3%
Uppercase Letter
ValueCountFrequency (%)
S 503
22.5%
D 481
21.5%
R 352
15.7%
E 270
12.1%
M 254
11.4%
T 109
 
4.9%
L 102
 
4.6%
H 78
 
3.5%
F 49
 
2.2%
A 23
 
1.0%
Space Separator
ValueCountFrequency (%)
928
100.0%
Other Punctuation
ValueCountFrequency (%)
& 109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16417
94.1%
Common 1037
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3179
19.4%
a 1536
 
9.4%
r 1136
 
6.9%
i 975
 
5.9%
c 907
 
5.5%
n 901
 
5.5%
t 900
 
5.5%
s 788
 
4.8%
l 733
 
4.5%
v 672
 
4.1%
Other values (22) 4690
28.6%
Common
ValueCountFrequency (%)
928
89.5%
& 109
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17454
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3179
18.2%
a 1536
 
8.8%
r 1136
 
6.5%
i 975
 
5.6%
928
 
5.3%
c 907
 
5.2%
n 901
 
5.2%
t 900
 
5.2%
s 788
 
4.5%
l 733
 
4.2%
Other values (24) 5471
31.3%
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Travel_Rarely
846 
Travel_Frequently
222 
Non-Travel
132 

Length

Max length17
Median length13
Mean length13.41
Min length10

Characters and Unicode

Total characters16092
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Frequently
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 846
70.5%
Travel_Frequently 222
 
18.5%
Non-Travel 132
 
11.0%

Length

2023-07-30T08:28:06.073239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:06.147839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 846
70.5%
travel_frequently 222
 
18.5%
non-travel 132
 
11.0%

Most occurring characters

ValueCountFrequency (%)
e 2490
15.5%
r 2268
14.1%
l 2268
14.1%
a 2046
12.7%
T 1200
7.5%
v 1200
7.5%
y 1068
6.6%
_ 1068
6.6%
R 846
 
5.3%
n 354
 
2.2%
Other values (7) 1284
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12492
77.6%
Uppercase Letter 2400
 
14.9%
Connector Punctuation 1068
 
6.6%
Dash Punctuation 132
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2490
19.9%
r 2268
18.2%
l 2268
18.2%
a 2046
16.4%
v 1200
9.6%
y 1068
8.5%
n 354
 
2.8%
q 222
 
1.8%
u 222
 
1.8%
t 222
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T 1200
50.0%
R 846
35.2%
F 222
 
9.2%
N 132
 
5.5%
Connector Punctuation
ValueCountFrequency (%)
_ 1068
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14892
92.5%
Common 1200
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2490
16.7%
r 2268
15.2%
l 2268
15.2%
a 2046
13.7%
T 1200
8.1%
v 1200
8.1%
y 1068
7.2%
R 846
 
5.7%
n 354
 
2.4%
F 222
 
1.5%
Other values (5) 930
 
6.2%
Common
ValueCountFrequency (%)
_ 1068
89.0%
- 132
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16092
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2490
15.5%
r 2268
14.1%
l 2268
14.1%
a 2046
12.7%
T 1200
7.5%
v 1200
7.5%
y 1068
6.6%
_ 1068
6.6%
R 846
 
5.3%
n 354
 
2.2%
Other values (7) 1284
8.0%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1658333
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-30T08:28:06.226475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1766363
Coefficient of variation (CV)0.89207778
Kurtosis-0.24201678
Mean9.1658333
Median Absolute Deviation (MAD)5
Skewness0.96295612
Sum10999
Variance66.85738
MonotonicityNot monotonic
2023-07-30T08:28:06.320731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 184
15.3%
1 170
14.2%
8 69
 
5.8%
3 67
 
5.6%
10 66
 
5.5%
9 66
 
5.5%
7 65
 
5.4%
5 54
 
4.5%
4 51
 
4.2%
6 46
 
3.8%
Other values (19) 362
30.2%
ValueCountFrequency (%)
1 170
14.2%
2 184
15.3%
3 67
 
5.6%
4 51
 
4.2%
5 54
 
4.5%
6 46
 
3.8%
7 65
 
5.4%
8 69
 
5.8%
9 66
 
5.5%
10 66
 
5.5%
ValueCountFrequency (%)
29 23
1.9%
28 20
1.7%
27 9
 
0.8%
26 22
1.8%
25 19
1.6%
24 23
1.9%
23 22
1.8%
22 17
1.4%
21 15
1.2%
20 19
1.6%
Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
449 
4
322 
2
239 
1
148 
5
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3 449
37.4%
4 322
26.8%
2 239
19.9%
1 148
 
12.3%
5 42
 
3.5%

Length

2023-07-30T08:28:06.399372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:06.493622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 449
37.4%
4 322
26.8%
2 239
19.9%
1 148
 
12.3%
5 42
 
3.5%

Most occurring characters

ValueCountFrequency (%)
3 449
37.4%
4 322
26.8%
2 239
19.9%
1 148
 
12.3%
5 42
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 449
37.4%
4 322
26.8%
2 239
19.9%
1 148
 
12.3%
5 42
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 449
37.4%
4 322
26.8%
2 239
19.9%
1 148
 
12.3%
5 42
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 449
37.4%
4 322
26.8%
2 239
19.9%
1 148
 
12.3%
5 42
 
3.5%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
367 
4
361 
2
242 
1
230 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row2
5th row1

Common Values

ValueCountFrequency (%)
3 367
30.6%
4 361
30.1%
2 242
20.2%
1 230
19.2%

Length

2023-07-30T08:28:06.574785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:06.657982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 367
30.6%
4 361
30.1%
2 242
20.2%
1 230
19.2%

Most occurring characters

ValueCountFrequency (%)
3 367
30.6%
4 361
30.1%
2 242
20.2%
1 230
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 367
30.6%
4 361
30.1%
2 242
20.2%
1 230
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 367
30.6%
4 361
30.1%
2 242
20.2%
1 230
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 367
30.6%
4 361
30.1%
2 242
20.2%
1 230
19.2%

EmpHourlyRate
Real number (ℝ)

Distinct71
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.981667
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-30T08:28:06.745122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.211302
Coefficient of variation (CV)0.30631694
Kurtosis-1.1868905
Mean65.981667
Median Absolute Deviation (MAD)18
Skewness-0.035164888
Sum79178
Variance408.49674
MonotonicityNot monotonic
2023-07-30T08:28:06.839275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 25
 
2.1%
79 25
 
2.1%
42 24
 
2.0%
57 23
 
1.9%
46 23
 
1.9%
92 22
 
1.8%
72 22
 
1.8%
45 22
 
1.8%
96 22
 
1.8%
94 21
 
1.8%
Other values (61) 971
80.9%
ValueCountFrequency (%)
30 13
1.1%
31 13
1.1%
32 19
1.6%
33 16
1.3%
34 6
 
0.5%
35 14
1.2%
36 17
1.4%
37 13
1.1%
38 10
0.8%
39 14
1.2%
ValueCountFrequency (%)
100 14
1.2%
99 19
1.6%
98 20
1.7%
97 18
1.5%
96 22
1.8%
95 17
1.4%
94 21
1.8%
93 13
1.1%
92 22
1.8%
91 14
1.2%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
724 
2
294 
4
112 
1
 
70

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 724
60.3%
2 294
24.5%
4 112
 
9.3%
1 70
 
5.8%

Length

2023-07-30T08:28:06.949195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:07.027828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 724
60.3%
2 294
24.5%
4 112
 
9.3%
1 70
 
5.8%

Most occurring characters

ValueCountFrequency (%)
3 724
60.3%
2 294
24.5%
4 112
 
9.3%
1 70
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 724
60.3%
2 294
24.5%
4 112
 
9.3%
1 70
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 724
60.3%
2 294
24.5%
4 112
 
9.3%
1 70
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 724
60.3%
2 294
24.5%
4 112
 
9.3%
1 70
 
5.8%

EmpJobLevel
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2
441 
1
440 
3
173 
4
90 
5
56 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row5
5th row2

Common Values

ValueCountFrequency (%)
2 441
36.8%
1 440
36.7%
3 173
 
14.4%
4 90
 
7.5%
5 56
 
4.7%

Length

2023-07-30T08:28:07.121988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:07.201168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2 441
36.8%
1 440
36.7%
3 173
 
14.4%
4 90
 
7.5%
5 56
 
4.7%

Most occurring characters

ValueCountFrequency (%)
2 441
36.8%
1 440
36.7%
3 173
 
14.4%
4 90
 
7.5%
5 56
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 441
36.8%
1 440
36.7%
3 173
 
14.4%
4 90
 
7.5%
5 56
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 441
36.8%
1 440
36.7%
3 173
 
14.4%
4 90
 
7.5%
5 56
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 441
36.8%
1 440
36.7%
3 173
 
14.4%
4 90
 
7.5%
5 56
 
4.7%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
4
378 
3
354 
2
237 
1
231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row1
4th row4
5th row1

Common Values

ValueCountFrequency (%)
4 378
31.5%
3 354
29.5%
2 237
19.8%
1 231
19.2%

Length

2023-07-30T08:28:07.279767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:07.376626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
4 378
31.5%
3 354
29.5%
2 237
19.8%
1 231
19.2%

Most occurring characters

ValueCountFrequency (%)
4 378
31.5%
3 354
29.5%
2 237
19.8%
1 231
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 378
31.5%
3 354
29.5%
2 237
19.8%
1 231
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 378
31.5%
3 354
29.5%
2 237
19.8%
1 231
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 378
31.5%
3 354
29.5%
2 237
19.8%
1 231
19.2%

NumCompaniesWorked
Real number (ℝ)

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.665
Minimum0
Maximum9
Zeros156
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-30T08:28:07.436573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4693838
Coefficient of variation (CV)0.92659806
Kurtosis0.068862995
Mean2.665
Median Absolute Deviation (MAD)1
Skewness1.0486348
Sum3198
Variance6.0978565
MonotonicityNot monotonic
2023-07-30T08:28:07.515272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 433
36.1%
0 156
 
13.0%
3 133
 
11.1%
2 123
 
10.2%
4 107
 
8.9%
7 60
 
5.0%
6 56
 
4.7%
5 53
 
4.4%
8 40
 
3.3%
9 39
 
3.2%
ValueCountFrequency (%)
0 156
 
13.0%
1 433
36.1%
2 123
 
10.2%
3 133
 
11.1%
4 107
 
8.9%
5 53
 
4.4%
6 56
 
4.7%
7 60
 
5.0%
8 40
 
3.3%
9 39
 
3.2%
ValueCountFrequency (%)
9 39
 
3.2%
8 40
 
3.3%
7 60
 
5.0%
6 56
 
4.7%
5 53
 
4.4%
4 107
 
8.9%
3 133
 
11.1%
2 123
 
10.2%
1 433
36.1%
0 156
 
13.0%

OverTime
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
847 
True
353 
ValueCountFrequency (%)
False 847
70.6%
True 353
29.4%
2023-07-30T08:28:07.593857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

EmpLastSalaryHikePercent
Real number (ℝ)

Distinct15
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.2225
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-30T08:28:07.656323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6259182
Coefficient of variation (CV)0.23819466
Kurtosis-0.29974078
Mean15.2225
Median Absolute Deviation (MAD)2
Skewness0.80865363
Sum18267
Variance13.147283
MonotonicityNot monotonic
2023-07-30T08:28:07.734928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
14 172
14.3%
11 169
14.1%
13 168
14.0%
12 155
12.9%
15 82
6.8%
18 73
6.1%
16 68
 
5.7%
17 67
 
5.6%
19 63
 
5.2%
20 50
 
4.2%
Other values (5) 133
11.1%
ValueCountFrequency (%)
11 169
14.1%
12 155
12.9%
13 168
14.0%
14 172
14.3%
15 82
6.8%
16 68
 
5.7%
17 67
 
5.6%
18 73
6.1%
19 63
 
5.2%
20 50
 
4.2%
ValueCountFrequency (%)
25 13
 
1.1%
24 18
 
1.5%
23 21
 
1.8%
22 47
3.9%
21 34
2.8%
20 50
4.2%
19 63
5.2%
18 73
6.1%
17 67
5.6%
16 68
5.7%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
379 
4
355 
2
247 
1
219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row2
5th row4

Common Values

ValueCountFrequency (%)
3 379
31.6%
4 355
29.6%
2 247
20.6%
1 219
18.2%

Length

2023-07-30T08:28:07.829945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:07.908505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 379
31.6%
4 355
29.6%
2 247
20.6%
1 219
18.2%

Most occurring characters

ValueCountFrequency (%)
3 379
31.6%
4 355
29.6%
2 247
20.6%
1 219
18.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 379
31.6%
4 355
29.6%
2 247
20.6%
1 219
18.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 379
31.6%
4 355
29.6%
2 247
20.6%
1 219
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 379
31.6%
4 355
29.6%
2 247
20.6%
1 219
18.2%
Distinct40
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.33
Minimum0
Maximum40
Zeros10
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-30T08:28:08.002711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.797228
Coefficient of variation (CV)0.68819311
Kurtosis0.80563333
Mean11.33
Median Absolute Deviation (MAD)4
Skewness1.0868619
Sum13596
Variance60.796764
MonotonicityNot monotonic
2023-07-30T08:28:08.112825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 159
 
13.2%
6 105
 
8.8%
8 85
 
7.1%
9 77
 
6.4%
5 71
 
5.9%
1 65
 
5.4%
7 61
 
5.1%
4 51
 
4.2%
12 37
 
3.1%
15 34
 
2.8%
Other values (30) 455
37.9%
ValueCountFrequency (%)
0 10
 
0.8%
1 65
5.4%
2 26
 
2.2%
3 34
 
2.8%
4 51
4.2%
5 71
5.9%
6 105
8.8%
7 61
5.1%
8 85
7.1%
9 77
6.4%
ValueCountFrequency (%)
40 1
 
0.1%
38 1
 
0.1%
37 3
 
0.2%
36 4
0.3%
35 2
 
0.2%
34 5
0.4%
33 7
0.6%
32 8
0.7%
31 7
0.6%
30 5
0.4%

TrainingTimesLastYear
Real number (ℝ)

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7858333
Minimum0
Maximum6
Zeros44
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-30T08:28:08.191451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2634462
Coefficient of variation (CV)0.4535254
Kurtosis0.56753103
Mean2.7858333
Median Absolute Deviation (MAD)1
Skewness0.5320732
Sum3343
Variance1.5962962
MonotonicityNot monotonic
2023-07-30T08:28:08.254013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 445
37.1%
3 413
34.4%
4 98
 
8.2%
5 98
 
8.2%
1 56
 
4.7%
6 46
 
3.8%
0 44
 
3.7%
ValueCountFrequency (%)
0 44
 
3.7%
1 56
 
4.7%
2 445
37.1%
3 413
34.4%
4 98
 
8.2%
5 98
 
8.2%
6 46
 
3.8%
ValueCountFrequency (%)
6 46
 
3.8%
5 98
 
8.2%
4 98
 
8.2%
3 413
34.4%
2 445
37.1%
1 56
 
4.7%
0 44
 
3.7%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
727 
2
294 
4
115 
1
 
64

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 727
60.6%
2 294
24.5%
4 115
 
9.6%
1 64
 
5.3%

Length

2023-07-30T08:28:08.332636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:08.411425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 727
60.6%
2 294
24.5%
4 115
 
9.6%
1 64
 
5.3%

Most occurring characters

ValueCountFrequency (%)
3 727
60.6%
2 294
24.5%
4 115
 
9.6%
1 64
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 727
60.6%
2 294
24.5%
4 115
 
9.6%
1 64
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 727
60.6%
2 294
24.5%
4 115
 
9.6%
1 64
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 727
60.6%
2 294
24.5%
4 115
 
9.6%
1 64
 
5.3%

ExperienceYearsAtThisCompany
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0775
Minimum0
Maximum40
Zeros36
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-30T08:28:08.506295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q310
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.236899
Coefficient of variation (CV)0.88122911
Kurtosis4.0579594
Mean7.0775
Median Absolute Deviation (MAD)3
Skewness1.789055
Sum8493
Variance38.89891
MonotonicityNot monotonic
2023-07-30T08:28:08.600255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 152
12.7%
1 138
11.5%
2 107
8.9%
3 105
8.8%
10 100
8.3%
4 88
 
7.3%
7 73
 
6.1%
6 66
 
5.5%
9 66
 
5.5%
8 63
 
5.2%
Other values (27) 242
20.2%
ValueCountFrequency (%)
0 36
 
3.0%
1 138
11.5%
2 107
8.9%
3 105
8.8%
4 88
7.3%
5 152
12.7%
6 66
5.5%
7 73
6.1%
8 63
5.2%
9 66
5.5%
ValueCountFrequency (%)
40 1
 
0.1%
37 1
 
0.1%
36 2
 
0.2%
34 1
 
0.1%
33 5
0.4%
32 3
0.2%
31 2
 
0.2%
30 1
 
0.1%
29 2
 
0.2%
27 2
 
0.2%

ExperienceYearsInCurrentRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2916667
Minimum0
Maximum18
Zeros190
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-30T08:28:08.694067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6137441
Coefficient of variation (CV)0.84203746
Kurtosis0.43802869
Mean4.2916667
Median Absolute Deviation (MAD)3
Skewness0.88815867
Sum5150
Variance13.059147
MonotonicityNot monotonic
2023-07-30T08:28:08.756463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 303
25.2%
0 190
15.8%
7 176
14.7%
3 107
 
8.9%
4 92
 
7.7%
8 78
 
6.5%
9 63
 
5.2%
1 46
 
3.8%
6 30
 
2.5%
5 29
 
2.4%
Other values (9) 86
 
7.2%
ValueCountFrequency (%)
0 190
15.8%
1 46
 
3.8%
2 303
25.2%
3 107
 
8.9%
4 92
 
7.7%
5 29
 
2.4%
6 30
 
2.5%
7 176
14.7%
8 78
 
6.5%
9 63
 
5.2%
ValueCountFrequency (%)
18 2
 
0.2%
17 3
 
0.2%
16 7
 
0.6%
15 4
 
0.3%
14 10
 
0.8%
13 10
 
0.8%
12 7
 
0.6%
11 18
 
1.5%
10 25
 
2.1%
9 63
5.2%

YearsSinceLastPromotion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1941667
Minimum0
Maximum15
Zeros469
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-30T08:28:08.850729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2215599
Coefficient of variation (CV)1.4682385
Kurtosis3.5390801
Mean2.1941667
Median Absolute Deviation (MAD)1
Skewness1.9749316
Sum2633
Variance10.378448
MonotonicityNot monotonic
2023-07-30T08:28:08.929567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 469
39.1%
1 297
24.8%
2 127
 
10.6%
7 62
 
5.2%
4 53
 
4.4%
3 45
 
3.8%
5 35
 
2.9%
6 24
 
2.0%
11 23
 
1.9%
9 16
 
1.3%
Other values (6) 49
 
4.1%
ValueCountFrequency (%)
0 469
39.1%
1 297
24.8%
2 127
 
10.6%
3 45
 
3.8%
4 53
 
4.4%
5 35
 
2.9%
6 24
 
2.0%
7 62
 
5.2%
8 11
 
0.9%
9 16
 
1.3%
ValueCountFrequency (%)
15 11
 
0.9%
14 5
 
0.4%
13 8
 
0.7%
12 9
 
0.8%
11 23
 
1.9%
10 5
 
0.4%
9 16
 
1.3%
8 11
 
0.9%
7 62
5.2%
6 24
 
2.0%

YearsWithCurrManager
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.105
Minimum0
Maximum17
Zeros215
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-07-30T08:28:09.008040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.541576
Coefficient of variation (CV)0.8627469
Kurtosis0.14820164
Mean4.105
Median Absolute Deviation (MAD)3
Skewness0.8131583
Sum4926
Variance12.542761
MonotonicityNot monotonic
2023-07-30T08:28:09.086641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 281
23.4%
0 215
17.9%
7 176
14.7%
3 103
 
8.6%
8 87
 
7.2%
4 85
 
7.1%
1 67
 
5.6%
9 53
 
4.4%
6 28
 
2.3%
5 26
 
2.2%
Other values (8) 79
 
6.6%
ValueCountFrequency (%)
0 215
17.9%
1 67
 
5.6%
2 281
23.4%
3 103
 
8.6%
4 85
 
7.1%
5 26
 
2.2%
6 28
 
2.3%
7 176
14.7%
8 87
 
7.2%
9 53
 
4.4%
ValueCountFrequency (%)
17 6
 
0.5%
16 2
 
0.2%
15 3
 
0.2%
14 2
 
0.2%
13 10
 
0.8%
12 14
 
1.2%
11 20
 
1.7%
10 22
 
1.8%
9 53
4.4%
8 87
7.2%

Attrition
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
1022 
True
178 
ValueCountFrequency (%)
False 1022
85.2%
True 178
 
14.8%
2023-07-30T08:28:09.164821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
874 
2
194 
4
132 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 874
72.8%
2 194
 
16.2%
4 132
 
11.0%

Length

2023-07-30T08:28:09.243728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T08:28:09.651589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 874
72.8%
2 194
 
16.2%
4 132
 
11.0%

Most occurring characters

ValueCountFrequency (%)
3 874
72.8%
2 194
 
16.2%
4 132
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 874
72.8%
2 194
 
16.2%
4 132
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 874
72.8%
2 194
 
16.2%
4 132
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 874
72.8%
2 194
 
16.2%
4 132
 
11.0%

Interactions

2023-07-30T08:28:02.885439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:51.911708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:52.921963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:54.142847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:55.201673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:56.200962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:57.258084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:58.559337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:59.631640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:00.740971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:01.815750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:02.968239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:52.008283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:53.008058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:54.228150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:55.288597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:56.294539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:57.351686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:58.650368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:59.734066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:00.835687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:01.920585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:03.073622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:52.101193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:53.088734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:54.318889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:55.377161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:56.394684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:57.458314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:58.734723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:59.835596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:00.934574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:01.999844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:03.175724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:52.188399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:53.187434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:54.417399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:55.468348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:56.494646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:57.558660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:58.837900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:59.937711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:01.030098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:02.114210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:03.270605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:52.274907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:53.279655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:54.510240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:55.552637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:56.586092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:57.655792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:58.920125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:00.018440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:01.131721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:02.206951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:03.617486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:52.360845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:53.553331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:54.606491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:55.635164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:56.669737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:57.750181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:59.032799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:00.120105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:01.215390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:02.298757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:03.714811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:52.458889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:53.656101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:54.708418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:55.734690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:56.768899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:58.060586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:59.134912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:00.233406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:01.334463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:02.397274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:03.813162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:52.546819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:53.738600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:54.802537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:55.825949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:56.866220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:58.149957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:59.232320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:00.326298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:01.430620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:02.481771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:03.916786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:52.628058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:53.836345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:54.906484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:55.924341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:56.974619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:58.259035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:59.337833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:00.436715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:01.531852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:02.582094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:04.016817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:52.734652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:53.944750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:55.001413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:56.017810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:57.068165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:58.359397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:59.441477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:00.540839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:01.615965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:02.682231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:04.115280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:52.821967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:54.045887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:55.103642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:56.113356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:57.157863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:58.463385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:27:59.531700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:00.633958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:01.731205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-30T08:28:02.782835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-07-30T08:28:09.745855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
AgeDistanceFromHomeEmpHourlyRateNumCompaniesWorkedEmpLastSalaryHikePercentTotalWorkExperienceInYearsTrainingTimesLastYearExperienceYearsAtThisCompanyExperienceYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerGenderEducationBackgroundMaritalStatusEmpDepartmentEmpJobRoleBusinessTravelFrequencyEmpEducationLevelEmpEnvironmentSatisfactionEmpJobInvolvementEmpJobLevelEmpJobSatisfactionOverTimeEmpRelationshipSatisfactionEmpWorkLifeBalanceAttritionPerformanceRating
Age1.000-0.0030.0610.3420.0010.6520.0010.2540.2030.1870.1960.0000.0000.1330.0000.1290.0470.1530.0620.0000.2950.0000.0000.0000.0460.2580.000
DistanceFromHome-0.0031.000-0.006-0.0090.0320.017-0.0120.0280.0230.0090.0170.0200.0000.0000.0000.0000.0500.0000.0320.0060.0600.0000.0800.0250.0000.0510.048
EmpHourlyRate0.061-0.0061.0000.034-0.0110.010-0.013-0.013-0.023-0.034-0.0020.0000.0120.0000.0000.0000.0000.0360.0000.0330.0000.0000.0640.0000.0000.0380.062
NumCompaniesWorked0.342-0.0090.0341.000-0.0030.313-0.040-0.173-0.132-0.064-0.1420.0000.0590.0480.0230.0720.0000.1000.0000.0000.1100.0000.0000.0000.0000.1100.000
EmpLastSalaryHikePercent0.0010.032-0.011-0.0031.000-0.016-0.015-0.039-0.023-0.055-0.0180.0280.0000.0000.0000.0070.0320.0160.0000.0230.0000.0000.0000.0330.0000.0000.478
TotalWorkExperienceInYears0.6520.0170.0100.313-0.0161.000-0.0130.5930.4990.3420.4900.0270.0320.0600.0000.2180.0000.1010.0000.0000.5390.0000.0000.0000.0340.2300.063
TrainingTimesLastYear0.001-0.012-0.013-0.040-0.015-0.0131.0000.0020.0140.034-0.0210.0000.0480.0080.0330.0110.0000.0420.0000.0000.0000.0250.0990.0000.0000.0320.000
ExperienceYearsAtThisCompany0.2540.028-0.013-0.173-0.0390.5930.0021.0000.8700.5200.8400.0470.0000.0380.0000.1290.0000.0760.0180.0620.3500.0000.0000.0000.0000.1840.103
ExperienceYearsInCurrentRole0.2030.023-0.023-0.132-0.0230.4990.0140.8701.0000.5130.7460.0660.0000.0520.0000.0900.0000.0400.0500.0030.2420.0260.0610.0000.0000.1800.191
YearsSinceLastPromotion0.1870.009-0.034-0.064-0.0550.3420.0340.5200.5131.0000.4560.0000.0000.0110.0000.0310.0410.0000.0000.0000.2110.0000.0000.0580.0000.0450.182
YearsWithCurrManager0.1960.017-0.002-0.142-0.0180.490-0.0210.8400.7460.4561.0000.0000.0000.0180.0000.0750.0840.0340.0000.0450.2270.0000.0000.0000.0380.1790.131
Gender0.0000.0200.0000.0000.0280.0270.0000.0470.0660.0000.0001.0000.0000.0110.0000.1040.0380.0480.0000.0000.0480.0000.0220.0000.0000.0170.000
EducationBackground0.0000.0000.0120.0590.0000.0320.0480.0000.0000.0000.0000.0001.0000.0000.3610.3330.0000.0630.0520.0000.0880.0500.0000.0460.0370.1020.009
MaritalStatus0.1330.0000.0000.0480.0000.0600.0080.0380.0520.0110.0180.0110.0001.0000.0390.0000.0490.0000.0360.0240.0540.0000.0000.0430.0000.1820.030
EmpDepartment0.0000.0000.0000.0230.0000.0000.0330.0000.0000.0000.0000.0000.3610.0391.0000.9750.0000.0000.0000.0370.1420.0330.0260.0530.0470.0590.159
EmpJobRole0.1290.0000.0000.0720.0070.2180.0110.1290.0900.0310.0750.1040.3330.0000.9751.0000.0000.0650.0000.0360.4140.0530.0700.0560.0650.1630.160
BusinessTravelFrequency0.0470.0500.0000.0000.0320.0000.0000.0000.0000.0410.0840.0380.0000.0490.0000.0001.0000.0000.0000.0400.0170.0000.0490.0000.0000.1240.013
EmpEducationLevel0.1530.0000.0360.1000.0160.1010.0420.0760.0400.0000.0340.0480.0630.0000.0000.0650.0001.0000.0000.0000.0870.0220.0000.0000.0000.0000.024
EmpEnvironmentSatisfaction0.0620.0320.0000.0000.0000.0000.0000.0180.0500.0000.0000.0000.0520.0360.0000.0000.0000.0001.0000.0400.0230.0000.0580.0000.0000.1310.365
EmpJobInvolvement0.0000.0060.0330.0000.0230.0000.0000.0620.0030.0000.0450.0000.0000.0240.0370.0360.0400.0000.0401.0000.0000.0000.0000.0000.0000.1500.000
EmpJobLevel0.2950.0600.0000.1100.0000.5390.0000.3500.2420.2110.2270.0480.0880.0540.1420.4140.0170.0870.0230.0001.0000.0000.0000.0000.0000.2170.038
EmpJobSatisfaction0.0000.0000.0000.0000.0000.0000.0250.0000.0260.0000.0000.0000.0500.0000.0330.0530.0000.0220.0000.0000.0001.0000.0470.0000.0000.0810.049
OverTime0.0000.0800.0640.0000.0000.0000.0990.0000.0610.0000.0000.0220.0000.0000.0260.0700.0490.0000.0580.0000.0000.0471.0000.0000.0000.2200.088
EmpRelationshipSatisfaction0.0000.0250.0000.0000.0330.0000.0000.0000.0000.0580.0000.0000.0460.0430.0530.0560.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
EmpWorkLifeBalance0.0460.0000.0000.0000.0000.0340.0000.0000.0000.0000.0380.0000.0370.0000.0470.0650.0000.0000.0000.0000.0000.0000.0000.0001.0000.0790.097
Attrition0.2580.0510.0380.1100.0000.2300.0320.1840.1800.0450.1790.0170.1020.1820.0590.1630.1240.0000.1310.1500.2170.0810.2200.0000.0791.0000.022
PerformanceRating0.0000.0480.0620.0000.4780.0630.0000.1030.1910.1820.1310.0000.0090.0300.1590.1600.0130.0240.3650.0000.0380.0490.0880.0000.0970.0221.000

Missing values

2023-07-30T08:28:04.283079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-30T08:28:04.769829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EmpNumberAgeGenderEducationBackgroundMaritalStatusEmpDepartmentEmpJobRoleBusinessTravelFrequencyDistanceFromHomeEmpEducationLevelEmpEnvironmentSatisfactionEmpHourlyRateEmpJobInvolvementEmpJobLevelEmpJobSatisfactionNumCompaniesWorkedOverTimeEmpLastSalaryHikePercentEmpRelationshipSatisfactionTotalWorkExperienceInYearsTrainingTimesLastYearEmpWorkLifeBalanceExperienceYearsAtThisCompanyExperienceYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttritionPerformanceRating
0E100100032MaleMarketingSingleSalesSales ExecutiveTravel_Rarely1034553241No124102210708No3
1E100100647MaleMarketingSingleSalesSales ExecutiveTravel_Rarely1444423212No12420237717No3
2E100100740MaleLife SciencesMarriedSalesSales ExecutiveTravel_Frequently544482315Yes21320231813112No4
3E100100941MaleHuman ResourcesDivorcedHuman ResourcesManagerTravel_Rarely1042732543No1522322216126No3
4E100101060MaleMarketingSingleSalesSales ExecutiveTravel_Rarely1641843218No14410132222No3
5E100101127MaleLife SciencesDivorcedDevelopmentDeveloperTravel_Frequently1024323311No2139429717No4
6E100101650MaleMarketingMarriedSalesSales RepresentativeTravel_Rarely844543127No1544232222No3
7E100101928FemaleLife SciencesSingleDevelopmentDeveloperTravel_Rarely121671127Yes13410437737Yes3
8E100102036FemaleLife SciencesMarriedDevelopmentDeveloperNon-Travel831634319No14110238705No3
9E100102138FemaleLife SciencesSingleDevelopmentDeveloperTravel_Rarely133813334Yes14410441000No3
EmpNumberAgeGenderEducationBackgroundMaritalStatusEmpDepartmentEmpJobRoleBusinessTravelFrequencyDistanceFromHomeEmpEducationLevelEmpEnvironmentSatisfactionEmpHourlyRateEmpJobInvolvementEmpJobLevelEmpJobSatisfactionNumCompaniesWorkedOverTimeEmpLastSalaryHikePercentEmpRelationshipSatisfactionTotalWorkExperienceInYearsTrainingTimesLastYearEmpWorkLifeBalanceExperienceYearsAtThisCompanyExperienceYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttritionPerformanceRating
1190E10098323MaleMedicalMarriedDevelopmentDeveloperTravel_Rarely431584112No1615343202No4
1191E10098525MaleLife SciencesMarriedSalesSales ExecutiveTravel_Rarely834574220No2234433212No4
1192E10098738FemaleMarketingSingleSalesSales ExecutiveTravel_Rarely744462240No2018237705No4
1193E10098829MaleLife SciencesDivorcedDevelopmentDeveloperTravel_Frequently142761141No184105310728No3
1194E10099048MaleMarketingMarriedSalesSales ExecutiveTravel_Rarely212564223No12412332222No3
1195E10099227FemaleMedicalDivorcedSalesSales ExecutiveTravel_Frequently314714241Yes2026336504No4
1196E10099337MaleLife SciencesSingleDevelopmentSenior DeveloperTravel_Rarely1024804143No1714231000No3
1197E10099450MaleMedicalMarriedDevelopmentSenior DeveloperTravel_Rarely2814744131Yes113203320838No3
1198E10099534FemaleMedicalSingleData ScienceData ScientistTravel_Rarely934462321No1429348777No3
1199E10099824FemaleLife SciencesSingleSalesSales ExecutiveTravel_Rarely321653239No1414332220Yes2